Exact Graph Structure Estimation with Degree Priors

We describe a generative model for graph edges under specific degree distributions which admits an exact and efficient inference method for recovering the most likely structure. This binary graph structure is obtained by reformulating the inference problem as a generalization of the polynomial time combinatorial optimization known as b-matching. Standard b-matching recovers a constant-degree constrained maximum weight subgraph from an original graph instead of a distribution over degrees. After this mapping, the most likely graph structure can be found in cubic time with respect to the number of nodes using max flow methods. Furthermore, in some instances, the combinatorial optimization problem can be solved exactly in near quadratic time by loopy belief propagation and max product updates even if the original input graph is dense. We show an example application to post-processing of recommender system predictions.